发布时间: 2021-09-08 22:53:00
题 目：Non-invasive inference of thrombus material properties with physics-informed neural network
报 告 人：郑小宁教授 暨南大学
时 间：2021-09-08 10:00--11:30
郑小宁,暨南大学信息科学技术学院数学系教授,于2012年在美国Purdue University 取得计算与应用数学博士学位，之后在麻省理工和布朗大学继续博士后研究工作。其主要研究方向是计算流体力学，分数阶模型，深度学习和数据驱动建模。
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring permeability and viscoelastic modulus from thrombus deformation data, which can be described by the fourth-order Cahn-Hilliard and Navier-Stokes Equations. In PINNs, the partial differential equations are encoded into a loss function, where partial derivatives can be obtained through automatic differentiation (AD). In addition to tackling the challenge of calculating the fourth-order derivative in the Cahn-Hilliard equation with AD, we introduce an auxiliary network along with the main neural network to approximate the second-derivative of the energy potential term. Our model can simultaneously predict unknown material parameters and velocity, pressure, and deformation gradient fields by merely training with partial information among all data.